Using autoregressive integrated moving average (ARIMA) models to predict and monitor the number of beds occupied during a SARS outbreak in a tertiary hospital in Singapore
By A. Earnest, Mark I. C. Chen, D. Ng, Leo Yee Sin. 2005
185 citations recorded by the source corpus.
BackgroundThe main objective of this study is to apply autoregressive integrated moving average (ARIMA) models to make real-time predictions on the number of beds occupied in Tan Tock Seng Hospital, during the recent SARS outbreak.MethodsThis is a retrospective study design. Hospital admission and occupancy data for isolation beds was collected from Tan Tock Seng hospital for the period 14th March 2003 to 31st May 2003. The main outcome measure was daily number of isolation beds occupied by SARS patients. Among the covariates considered were daily number of people screened, daily number of people admitted (including observation, suspect and probable cases) and days from the most recent significant event discovery. We utilized the following strategy for the analysis. Firstly, we split the outbreak data into two. Data from 14th March to 21st April 2003 was used for model development. We used structural ARIMA models in an attempt to model the number of beds occupied. Estimation is via the maximum likelihood method using the Kalman filter. For the ARIMA model parameters, we considered the simplest parsimonious lowest order model.ResultsWe found that the ARIMA (1,0,3) model was able to describe and predict the number of beds occupied during the SARS outbreak well. The mean absolute percentage error (MAPE) for the training set and validation set were 5.7% and 8.6% respectively, which we found was reasonable for use in the hospital setting. Furthermore, the model also provided three-day forecasts of the number of beds required. Total number of admissions and probable cases admitted on the previous day were also found to be independent prognostic factors of bed occupancy.ConclusionARIMA models provide useful tools for administrators and clinicians in planning for real-time bed capacity during an outbreak of an infectious disease such as SARS. The model could well be used in planning for bed-capacity during outbreaks of other infectious diseases as well.
Subfields: Emergency Medical Services, General Health Professions, Modeling and Simulation
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Citations in the corpus
- Forecasting Economics and Financial Time Series: ARIMA vs. LSTM (2018, methodology): “Even though ARIMA models are very pervalent in modeling economical and financial time series [Banerjee 2005; Khashei and Bijari, 2011; Alonso and Garc´ıa-Martos, 2012; Adebiyi et al. 2014], they have some major limitations [Armstrong 2001; Earnest et al. 2005;].”
- A Comparison of ARIMA and LSTM in Forecasting Time Series (2018, background): “Even though ARIMA models are very prevalent in modeling economical and financial time series [1], [2], [14], they have some major limitations [6].”
- The Performance of LSTM and BiLSTM in Forecasting Time Series (2019, background): “While these moving averaged-based approaches perform reasonably well, they also suffer from some limitations [8]: –”